
%run ./setup_notebook.ipynb


!pip install wordsegment

Requirement already satisfied: wordsegment in c:\users\harri\miniconda3\envs\py310_notebook_env\lib\site-packages (1.3.1)


# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from collections import defaultdict
import json
import ast
from tqdm import tqdm 
import time
import requests
from bs4 import BeautifulSoup, SoupStrainer
import pprint
import logging
import re
# from kaggle_secrets import UserSecretsClient
import plotly.express as px
import plotly.io as pio
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import swifter
import itertools
from geopy.geocoders import Nominatim
from wordsegment import load, segment
# from nltk.stem.snowball import SnowballStemmer
# from nltk.corpus import stopwords
# from nltk.tokenize import word_tokenize
# from nltk.stem import WordNetLemmatizer
 
 
# STOPWORDS = set(stopwords.words('english'))
px.defaults.template = 'bnw'


df_linkedin_listing_usa = pd.read_csv("../linkedin-data-analyst-jobs-listings/linkedin-jobs-usa.csv")
df_linkedin_listing_usa["country"] = "USA"


df_linkedin_listing_canada = pd.read_csv("../linkedin-data-analyst-jobs-listings/linkedin-jobs-canada.csv")
df_linkedin_listing_canada["country"] = "Canada"


assert list(df_linkedin_listing_usa.columns) == list(df_linkedin_listing_canada.columns), \
"Columns not equal, cannot concat vertically"


df_linkedin_listing = pd.concat([df_linkedin_listing_usa, df_linkedin_listing_canada])


df_linkedin_listing.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 5618 entries, 0 to 2772
Data columns (total 10 columns):
 #   Column         Non-Null Count  Dtype 
---  ------         --------------  ----- 
 0   title          5618 non-null   object
 1   company        5618 non-null   object
 2   description    5618 non-null   object
 3   onsite_remote  5618 non-null   object
 4   salary         965 non-null    object
 5   location       5618 non-null   object
 6   criteria       5618 non-null   object
 7   posted_date    5618 non-null   object
 8   link           5618 non-null   object
 9   country        5618 non-null   object
dtypes: object(10)
memory usage: 482.8+ KB


df_linkedin_listing.head(5)


df_linkedin_listing.head(1)


criteria_records = []
for criteria_string_form in df_linkedin_listing["criteria"]:
    criteria_array = None
    try:
        criteria_array = ast.literal_eval(criteria_string_form)
    except Exception as e:
        print(e)
        print(criteria_string_form)
        continue    
    criteria_records.append({k:v for criteria_dict in criteria_array for k,v in criteria_dict.items()})

# criteria_df = df_linkedin_listing.iloc[0:3].apply(convert_jsons_to_table, axis=1)
criteria_records[0]

{'Seniority level': 'Not Applicable',
 'Employment type': 'Full-time',
 'Job function': 'Information Technology',
 'Industries': 'Software Development, Technology, Information and Internet, and Financial Services'}


criteria_df = pd.DataFrame.from_records(criteria_records)


# Validation Cell
criteria_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5618 entries, 0 to 5617
Data columns (total 4 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   Seniority level  5194 non-null   object
 1   Employment type  5541 non-null   object
 2   Job function     5194 non-null   object
 3   Industries       5193 non-null   object
dtypes: object(4)
memory usage: 175.7+ KB


df = pd.merge(df_linkedin_listing, criteria_df, left_index=True, right_index=True)


df.head(1)


df.drop("criteria", axis=1, inplace=True)


df.reset_index(drop=True, inplace=True)


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5618 entries, 0 to 5617
Data columns (total 13 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   title            5618 non-null   object
 1   company          5618 non-null   object
 2   description      5618 non-null   object
 3   onsite_remote    5618 non-null   object
 4   salary           965 non-null    object
 5   location         5618 non-null   object
 6   posted_date      5618 non-null   object
 7   link             5618 non-null   object
 8   country          5618 non-null   object
 9   Seniority level  5241 non-null   object
 10  Employment type  5527 non-null   object
 11  Job function     5241 non-null   object
 12  Industries       5239 non-null   object
dtypes: object(13)
memory usage: 570.7+ KB


df["Seniority level"] = df["Seniority level"].replace("Not Applicable", np.nan)
df["Seniority level"].info()

<class 'pandas.core.series.Series'>
RangeIndex: 5618 entries, 0 to 5617
Series name: Seniority level
Non-Null Count  Dtype 
--------------  ----- 
3841 non-null   object
dtypes: object(1)
memory usage: 44.0+ KB


df.to_csv("Linkedin_Jobs_American_and_Canadian.csv")


# !pip install -q --upgrade wandb


# # Import wandb
# import wandb

# try:
#     from kaggle_secrets import UserSecretsClient
#     user_secrets = UserSecretsClient()
#     secret_value_0 = user_secrets.get_secret("wandb_api")
#     wandb.login(key=secret_value_0)
    
#     anony=None
# except Exception as e:
    
#     anony = "must"
#     print('If you want to use your W&B account, go to Add-ons -> Secrets and add your W&B access token. Use the Label name as "wandb_api". \nGet your W&B access token from here: https://wandb.ai/authorize')


# run = wandb.init(project='eda', anonymous=None) # W&B Code 1


# # Initialize a W&B run to log images
# data_at = wandb.Table(columns=df.columns.tolist()) # W&B Code 2
# for i in tqdm(range(len(df))):
#     row = df.loc[i]
#     data_at.add_data(*tuple(row.values[0:])) # W&B Code 3

# wandb.log({'LinkedIn Job Data': data_at}) # W&B Code 4
# wandb.finish() # W&B Code 5


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5618 entries, 0 to 5617
Data columns (total 13 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   title            5618 non-null   object
 1   company          5618 non-null   object
 2   description      5618 non-null   object
 3   onsite_remote    5618 non-null   object
 4   salary           965 non-null    object
 5   location         5618 non-null   object
 6   posted_date      5618 non-null   object
 7   link             5618 non-null   object
 8   country          5618 non-null   object
 9   Seniority level  3841 non-null   object
 10  Employment type  5527 non-null   object
 11  Job function     5241 non-null   object
 12  Industries       5239 non-null   object
dtypes: object(13)
memory usage: 570.7+ KB


fig1 = px.pie(df["title"].value_counts().reset_index(), names="index", values="title", color="index",
             color_discrete_sequence=px.colors.qualitative.Pastel2)
remove_px_attributes(fig1)
fig1.update_traces(textinfo=None, textposition="inside")
fig1.add_annotation(text=f"Sample Size: {sum(~df['title'].isnull())}", xref="paper", yref="paper", xanchor="right",
                   yanchor="top", x=1, y=-0.1, ax=0, ay=0)
fig1.update_layout(
    uniformtext_minsize=9, uniformtext_mode="hide",
    title=dict(
    text="Types of Jobs in Dataset",
    x=0.05,
    xanchor="left"
), margin=dict(t=100))


df["posted_date"] = df["posted_date"].astype(str)


fig2 = px.line(df["posted_date"].value_counts().sort_index())
fig2.update_yaxes(title="Number of Jobs")
fig2.update_xaxes(title="Posted Date")
fig2.update_layout(title="Number of Jobs Over Time", margin=dict(r=50))
fig2.update_traces(hovertemplate="<b>Date:</b> %{x}<br><b>Number of Jobs:</b> %{y}<br>", mode="lines+markers")
fig2.add_annotation(text=f"Sample Size: {sum(~df['posted_date'].isnull())}", xref="paper", yref="paper", xanchor="right",
                   yanchor="top", x=1, y=-0.1, ax=0, ay=0)
fig2


fig3 = px.histogram(df, x="onsite_remote", color="onsite_remote")
remove_px_attributes(fig3)
add_annotation_for_figure(fig3, f"Sample Size:  {sum(~df['onsite_remote'].isnull())}", 
                          x_anchor="right", x=1)
fig3.update_yaxes(title=dict(text="count", standoff=20))
fig3.update_xaxes(title="onsite_remote")
fig3.update_layout(title="Number of Jobs by Work Style", margin=dict(l=100), coloraxis_showscale=False)


fig4 = px.pie(df["Industries"].value_counts().reset_index(), names="index", values="Industries",
              color_discrete_sequence=px.colors.qualitative.Set3)
remove_px_attributes(fig4)
add_annotation_for_figure(fig4, f"Sample Size:  {sum(df['Industries'].isnull() == False)}", 
                          x_anchor="right", x=1)
fig4.update_layout(
    uniformtext_minsize=12, uniformtext_mode="hide", title=dict(
    text="Jobs by Sector",
    xanchor="left",
    font_size=20,
    x=0.05,
    y=0.95)
)
fig4.update_traces(textposition="inside", texttemplate="%{percent}")
fig4

# for i, trace in enumerate(fig4.data):
#     print(trace["name"])
#     if not trace["name"] in items_to_show:
#         fig4.data[i]["showlegend"] = False


fig5 = px.pie(df["Job function"].value_counts().reset_index(), names="index", values="Job function",
              color_discrete_sequence=px.colors.qualitative.Set3)
remove_px_attributes(fig5)
add_annotation_for_figure(fig5, f"Sample Size:  {sum(df['Job function'].isnull() == False)}", 
                          x_anchor="right", x=1)
fig5.update_layout(
    uniformtext_minsize=12, uniformtext_mode="hide", title=dict(
    text="Jobs by Function",
    xanchor="left",
    font_size=20,
    x=0.05,
    y=0.95)
)
fig5.update_traces(textposition="inside", texttemplate="%{percent}")
fig5

# for i, trace in enumerate(fig4.data):
#     print(trace["name"])
#     if not trace["name"] in items_to_show:
#         fig4.data[i]["showlegend"] = False


fig6 = px.histogram(df, x="Employment type", color="Employment type")
remove_px_attributes(fig6)
add_annotation_for_figure(fig6, f"Jobs by Employment Type:  {sum(df['Employment type'].isnull() == False)}", 
                          x_anchor="right", x=1)
fig6.update_yaxes(title=dict(text="count", standoff=20))
fig6.update_xaxes(title="Employment type")
fig6.update_layout(title="Number of Jobs by Employment Type", margin=dict(l=100), coloraxis_showscale=False)


df.loc[~df["salary"].isnull(), "salary"]

16      $100,000.00\r\n            -\r\n            $1...
48      $50,000.00\r\n            -\r\n            $55...
70      $100,000.00\r\n            -\r\n            $1...
122     $100,000.00\r\n            -\r\n            $1...
176     $100,000.00\r\n            -\r\n            $1...
                              ...                        
5607        $30.00\r\n            -\r\n            $33.00
5609    $120,000.00\r\n            -\r\n            $1...
5614    $75,000.00\r\n            -\r\n            $95...
5616    $85,000.00\r\n            -\r\n            $95...
5617    $130,000.00\r\n            -\r\n            $1...
Name: salary, Length: 965, dtype: object


df_salary = pd.DataFrame()
salary_data = df["salary"].replace("[\r\n\s,]+", "", regex=True) \
                          .replace("CA", "", regex=True) # replace spaces and unexpected text

salaries = salary_data.str.split("-")

## Left bound and upper bound salaries
df_salary["salary_lb"] = salaries.str[0].str.strip("$").astype(float)
df_salary["salary_ub"] = salaries.str[1].str.strip("$").astype(float)
df_salary["salary_text"] = salary_data


df_salary.head()


df_salary.describe()


fig = px.histogram(df_salary, x="salary_lb")
fig.update_layout(title="Distribution of Salary (Lower Bound Distribution)")
add_annotation_for_figure(fig, f"Sample Size: {sum(~df_salary['salary_lb'].isnull())}")
fig


df_salary_hourly = df_salary.loc[df_salary["salary_lb"] <= 2000]


df_salary_hourly.describe()


df_salary.loc[(df_salary["salary_lb"] >= 3000) & (df_salary["salary_lb"] <= 7000)]


df_salary.loc[df_salary["salary_lb"] <= 2000, ["salary_lb", "salary_ub"]]  = df_salary.loc[df_salary["salary_lb"] <= 2000, ["salary_lb", "salary_ub"]] * 40 * 4 * 12


df_salary.loc[(df_salary["salary_lb"] >= 3000) & (df_salary["salary_lb"] <= 7000), ["salary_lb", "salary_ub"]] = \
df_salary.loc[(df_salary["salary_lb"] >= 3000) & (df_salary["salary_lb"] <= 7000), ["salary_lb", "salary_ub"]] * 12


df_salary["salary_text"] = "$" + df_salary["salary_lb"].astype(str) + "-" + df_salary["salary_ub"].astype(str)


fig = px.histogram(df_salary, x="salary_lb")
fig.update_layout(title="Distribution of Salary (Lower Bound Distribution)")
add_annotation_for_figure(fig, f"Sample Size: {sum(~df_salary['salary_lb'].isnull())}")
fig


fig = px.histogram(df_salary, x="salary_ub")
fig.update_layout(title="Distribution of Salary (Lower Bound Distribution)")
add_annotation_for_figure(fig, f"Sample Size: {sum(~df_salary['salary_ub'].isnull())}")
fig


df_salary_analysis = df_salary.merge(df, left_index=True, right_index=True)


for column in df_salary_analysis:
    print(f"{column}:", len(df_salary_analysis[column].unique()))

salary_lb: 29
salary_ub: 34
salary_text: 49
title: 135
company: 305
description: 362
onsite_remote: 3
salary: 49
location: 148
posted_date: 50
link: 5618
country: 2
Seniority level: 5
Employment type: 5
Job function: 54
Industries: 78


df_salary_analysis["Seniority level"].unique()

array([nan, 'Entry level', 'Associate', 'Mid-Senior level', 'Executive'],
      dtype=object)


df_salary_analysis["Employment type"].unique()

array(['Full-time', nan, 'Contract', 'Temporary', 'Volunteer'],
      dtype=object)


df_salary_analysis.groupby("Seniority level").agg({"salary_ub": "mean"})


encode_df = df_salary_analysis.copy()


encode_df["Seniority level"].replace({"Entry level" : 1, "Associate" : 2, "Mid-Senior level" : 3, "Executive" : 4}, inplace=True)
encode_df["Employment type"].replace({"Volunteer" : 1, "Temporary" : 2, "Contract" : 3, "Full-time" : 4}, inplace=True)


fig = px.imshow(encode_df.corr(), color_continuous_scale="BuGn")
fig.update_layout(title=dict(text="Correlations Between Job Attributes", x=0.05, y=0.96, xanchor="left", font_size=24),
                  margin=dict(b=160))


fig7 = px.box(df_salary_analysis, x="salary_lb", color="Seniority level")
remove_px_attributes(fig7)
add_annotation_for_figure(fig7, f"Sample Size: {min(sum(~df_salary_analysis['salary_lb'].isnull()), sum(~df_salary_analysis['Seniority level'].isnull()))}",
                          x_anchor="center", x=1.1)
fig7.update_layout(showlegend=True, title="Salary (Lower Bound) Based on Seniority Level")
fig7.update_xaxes(showgrid=True)
fig7.update_traces(hoverinfo="x")


from plotly.subplots import make_subplots
class BreakLoop(Exception):
    pass

def generate_subplots(figures, custom_specs=None, desired_rows=None, desired_columns=None, titles=None,
                      auto_position=True, custom_positions=None):

    '''
        For each figure, append their traces to new subplot figure
        based on the correct row and col indices
    '''
    NUM_FIGURES = len(figures)
    DESIRED_ROWS = desired_rows
    DESIRED_COLUMNS = desired_columns
    # num_traces = sum([len(figure.data) for figure in figures])
    # if desired_rows is not None:
    #     desired_columns = num_figures // desired_rows + 1 

    # if desired_columns is not None:
    #     desired_rows = num_figures // desired_columns + 1

    specs = None
    specs_positions = None
    
    
    ######### Default Specs ######################
    if custom_specs is None:
        
        specs = np.full((DESIRED_ROWS, DESIRED_COLUMNS), {})
        specs_positions = [(i,j) for i in DESIRED_ROWS for j in DESIRED_COLUMNS]
        k = 0

        # Populate all figures from top-left to bottom-right into specs
        for i in range(DESIRED_ROWS):
            for j in range(DESIRED_COLUMNS):
                new_dict = {}
                new_dict["type"] = figures[k]["data"][0]["type"]
                specs[i][j] = new_dict
                k += 1
            
            if k >= NUM_FIGURES:
                break

        specs = specs.tolist()
    
    ######## Custom Specs ########################
    else:
        specs = custom_specs
         
        # find specs positions based on col_span and row_span
        specs_positions = []

        if auto_position:
            try:
                for i, spec_row in enumerate(specs):
                    for j, spec_value in enumerate(spec_row):
                        if spec_value is None:
                            continue
                        
                        # find first position
                        specs_positions.append((i+1, j+1))
                        raise BreakLoop("Broke out of Pythonic Loop") # Python is very bad at breaking out of a double for loop
                    
            except BreakLoop as e:
                pass
    
            row, col = specs_positions.pop()
            
            # Work with Pythonic grid instead of Plotly grid, which starts at (1,1) top-left instead of (0,0) top-left
            row -= 1
            col -= 1

            for _ in range(NUM_FIGURES):
                this_figure_row = row
                this_figure_column = col
                specs_dict = specs[row][col]
                specs_positions.append((row + 1, col + 1))
                if specs_dict is None:
                    raise Exception(
                        "Your figures should not be drawn in an empty subplot. Please make sure to indicate the correct number of blank rows/cols"
                        "or colspan/rowspan going after a figure if necessary" 
                    )

                # add col_blank + row_blank positional changes and remove attributes from specs dictionary
                if "col_blank" in specs_dict:
                    col += (specs_dict["col_blank"] + 1)
                    del specs_dict["col_blank"]

                if "row_blank" in specs_dict:
                    row += (specs_dict["row_blank"] + 1)
                    del specs_dict["row_blank"]
                 
                specs[this_figure_row][this_figure_column] = specs_dict

                # Adjust implicitly and explicity to colspan and rowspan
                if "colspan" in specs_dict:
                    col += specs_dict["colspan"]
                else:
                    col += 1
                
                
                if "rowspan" in specs_dict:
                    # rowspan moves independently of col operations
                    row += specs_dict["rowspan"]
                else:
                    if col >= DESIRED_COLUMNS:
                        row += 1
                
                if col >= DESIRED_COLUMNS:                 
                    col = 0
        else:
            specs_positions = custom_positions
                
    all_titles = []
    if titles is None:
         all_titles = [fig.layout.title.text if "title" in fig.layout else '' for fig in figures]
             

    fig = make_subplots(DESIRED_ROWS, DESIRED_COLUMNS,
                        specs=specs,
                        subplot_titles=all_titles
                        )

    assert len(specs_positions) == len(figures), "Length of specs positions not the same as number of figures"
    for i, figure in enumerate(figures):
        specs_row, specs_col = specs_positions[i]
        for trace in figure["data"]:
            fig.append_trace(
                trace, row=specs_row, col=specs_col
            )

        col += 1
    fig.update_layout(template="bnw")

    return fig

# fig1 = go.Figure(go.Scatter(x=[1,2,3], y=[4,5,6], mode="lines+markers"))
# fig2 = px.line(x=[1,2,3], y=[7,8,9])
# fig2.update_traces(line=dict(color="firebrick"), mode="lines")
# remove_px_attributes(fig2, remove_hovertemplate=True, remove_mode=False)
# fig3 = px.bar(pd.DataFrame({"a" : [1,1,3], "b": [7,8,9], "c" : ["blue", "brown", "brown"]}),
#               x="a", y="b", color="c")
# remove_px_attributes(fig3)


# specs=[
#     [{}, {}],
#     [{"colspan": 2}, None]
# ]
# figs = [fig1, fig2, fig3]
# generate_subplots(figs, custom_specs=specs, desired_rows=2)
# help(make_subplots)


figures = [fig1, fig4, fig5, fig2, fig3, fig6, fig7]
specs = [
    [{"type" : "pie", "colspan" : 2}, None, {"type" : "pie", "colspan" : 2}, None, {"type" : "pie", "colspan" : 2}, None],
    [{"type" : "scatter", "colspan" : 6}, None, None, None, None, None],
    [{"type" : "histogram", "colspan" : 3}, None, None, {"type" : "histogram", "colspan" : 3}, None, None],
    [{"type" : "box", "colspan" : 6, "rowspan" : 2}, None, None, None, None, None],
    [None, None, None, None,None, None]
]

fig = generate_subplots(figures, desired_rows = 5, desired_columns = 6, custom_specs=specs)
fig.update_layout(height=1000, title=dict(
        text="Linkedin Job Dataset Data Report",
        xanchor="left",
        x=0.05,
        font_size=50
    ),
    margin=dict(t=200),
)


go.FigureWidget(data=fig7["data"], layout=fig7["layout"])

FigureWidget({
    'data': [{'alignmentgroup': 'True',
              'hoverinfo': 'x',
              'legendgroup': 'Entry level',
              'marker': {'color': '#FF7F0E'},
              'name': 'Entry level',
              'notched': False,
              'offsetgroup': 'Entry level',
              'orientation': 'h',
              'showlegend': True,
              'type': 'box',
              'uid': 'c76cfe63-000c-46db-8ef5-8d65805a782a',
              'x': array([   nan,    nan,    nan, ...,    nan, 42240., 57600.]),
              'x0': ' ',
              'xaxis': 'x',
              'y0': ' ',
              'yaxis': 'y'},
             {'alignmentgroup': 'True',
              'hoverinfo': 'x',
              'legendgroup': 'Associate',
              'marker': {'color': '#2CA02C'},
              'name': 'Associate',
              'notched': False,
              'offsetgroup': 'Associate',
              'orientation': 'h',
              'showlegend': True,
              'type': 'box',
              'uid': '0b024056-e262-41ca-acc0-73fccc60a03b',
              'x': array([100000.,     nan, 100000., ...,     nan, 120000.,     nan]),
              'x0': ' ',
              'xaxis': 'x',
              'y0': ' ',
              'yaxis': 'y'},
             {'alignmentgroup': 'True',
              'hoverinfo': 'x',
              'legendgroup': 'Mid-Senior level',
              'marker': {'color': '#D62728'},
              'name': 'Mid-Senior level',
              'notched': False,
              'offsetgroup': 'Mid-Senior level',
              'orientation': 'h',
              'showlegend': True,
              'type': 'box',
              'uid': '24f47108-9ca3-43b6-a5b9-bc9766a04582',
              'x': array([    nan,     nan,     nan, ...,     nan,  75000., 130000.]),
              'x0': ' ',
              'xaxis': 'x',
              'y0': ' ',
              'yaxis': 'y'},
             {'alignmentgroup': 'True',
              'hoverinfo': 'x',
              'legendgroup': 'Executive',
              'marker': {'color': '#9467BD'},
              'name': 'Executive',
              'notched': False,
              'offsetgroup': 'Executive',
              'orientation': 'h',
              'showlegend': True,
              'type': 'box',
              'uid': '5ede6670-f04e-4f8b-80f9-cb5786c15f88',
              'x': array([nan, nan]),
              'x0': ' ',
              'xaxis': 'x',
              'y0': ' ',
              'yaxis': 'y'}],
    'layout': {'annotations': [{'ax': 0,
                                'ay': 0,
                                'font': {'size': 16},
                                'text': 'Sample Size: 965',
                                'x': 1.1,
                                'xanchor': 'center',
                                'xref': 'paper',
                                'y': -0.1,
                                'yanchor': 'top',
                                'yref': 'paper'}],
               'boxmode': 'group',
               'legend': {'title': {'text': 'Seniority level'}, 'tracegroupgap': 0},
               'margin': {'t': 60},
               'showlegend': True,
               'template': '...',
               'title': {'text': 'Salary (Lower Bound) Based on Seniority Level'},
               'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'showgrid': True, 'title': {'text': 'salary_lb'}},
               'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0]}}
})


from ipywidgets import Button, Layout, jslink, IntText, IntSlider, GridspecLayout, HBox, VBox

grid = GridspecLayout(4, 1)

def create_expanded_button(description, button_style):
    return Button(description=description, button_style=button_style, layout=Layout(height='auto', width='auto'))

for i in range(4):
    for j in range(1):
        grid[i, j] = create_expanded_button('Button {} - {}'.format(i, j), 'warning')
grid

GridspecLayout(children=(Button(button_style='warning', description='Button 0 - 0', layout=Layout(grid_area='w…


def convert_figures_to_figurewidgets(figures : list[go.Figure]):
    return [go.FigureWidget(data=[trace.to_plotly_json() for trace in figure["data"]], 
                            layout=figure["layout"]) for figure in figures]

def generate_gridspec(figures, custom_specs=None, desired_rows=None, desired_columns=None, titles=None,
                      auto_position=True, custom_positions=None):
    '''
        1. Convert figures into FigureWidgets
        2. Prepare GridSpec
        3. Traverse all figures through the specs object
            a. For each figure traversed, add it to GridSpec
    '''
        
    NUM_FIGURES = len(figures)
    DESIRED_ROWS = desired_rows
    DESIRED_COLUMNS = desired_columns
    # num_traces = sum([len(figure.data) for figure in figures])
    # if desired_rows is not None:
    #     desired_columns = num_figures // desired_rows + 1 

    # if desired_columns is not None:
    #     desired_rows = num_figures // desired_columns + 1

    specs = None
    specs_positions = None
    figures = convert_figures_to_figurewidgets(figures)
    grid = GridspecLayout(DESIRED_ROWS, DESIRED_COLUMNS)


    # find specs positions based on col_span and row_span
    if custom_specs:
        try:
            for i, spec_row in enumerate(specs):
                for j, spec_value in enumerate(spec_row):
                    if spec_value is None:
                        continue
                    
                    # find first position
                    specs_positions.append((i+1, j+1))
                    raise BreakLoop("Broke out of Pythonic Loop") # Python is very bad at breaking out of a double for loop
                
        except BreakLoop as e:
            pass

        row, col = specs_positions.pop()
        
        # Work with Pythonic grid instead of Plotly grid, which starts at (1,1) top-left instead of (0,0) top-left
        row -= 1
        col -= 1

        for i in range(NUM_FIGURES):
            this_figure_row = row
            this_figure_column = col
            specs_dict = specs[row][col]
            if specs_dict is None:
                raise Exception(
                    "Your figures should not be drawn in an empty subplot. Please make sure to indicate the correct number of blank rows/cols"
                    "or colspan/rowspan going after a figure if necessary" 
                )

            # add col_blank + row_blank positional changes and remove attributes from specs dictionary
            if "col_blank" in specs_dict:
                col += (specs_dict["col_blank"] + 1)

            if "row_blank" in specs_dict:
                row += (specs_dict["row_blank"] + 1)

            # Adjust implicitly and explicity to colspan and rowspan
            if "colspan" in specs_dict:
                col += specs_dict["colspan"]
            else:
                col += 1
            
            
            if "rowspan" in specs_dict:
                # rowspan moves independently of col operations
                row += specs_dict["rowspan"]
            else:
                if col >= DESIRED_COLUMNS:
                    row += 1
            
            if col >= DESIRED_COLUMNS:                 
                col = 0

            if this_figure_row == row:
                grid[row, this_figure_column:column] = figures[i]
            else:
                grid[this_figure_row:row, this_figure_column:column] = figures[i]
            
    else:
        specs = np.full((DESIRED_ROWS, DESIRED_COLUMNS), {})
        k = 0
        # Populate all figures from top-left to bottom-right into specs
        for i in range(DESIRED_ROWS):
            for j in range(DESIRED_COLUMNS):
                grid[i, j] = figures[k]
                k += 1
            
            if k >= NUM_FIGURES:
                break
    
    return grid


jup_figures = convert_figures_to_figurewidgets(figures)

c:\Users\harri\miniconda3\envs\py310_notebook_env\lib\site-packages\jupyter_client\session.py:719: UserWarning:

Message serialization failed with:
Out of range float values are not JSON compliant
Supporting this message is deprecated in jupyter-client 7, please make sure your message is JSON-compliant


jup_figure_1 = jup_figures[0]


jup_figure_1_data = jup_figures[0]["data"]
jup_figure_1_data_json = jup_figure_1_data[0].to_plotly_json()
jup_figure_1_data_json.keys()

dict_keys(['customdata', 'domain', 'labels', 'legendgroup', 'marker', 'name', 'showlegend', 'textposition', 'values', 'type', 'uid'])


pie_charts = VBox([jup_figures[0], jup_figures[1], jup_figures[2]])
bar_charts = VBox([jup_figures[4], jup_figures[5]])
time_chart = jup_figures[3]
salary_chart_by_seniority = jup_figures[6]


report = VBox([pie_charts, bar_charts, time_chart, salary_chart_by_seniority], layout=dict(height="3000px", margin="0px 0px 0px 0px", padding="0px 0px 0px 0px"))


from IPython.display import display
display(report)

VBox(children=(VBox(children=(FigureWidget({
    'data': [{'customdata': array([['Data Analyst'],
            …


from ipywidgets import IntSlider
from ipywidgets.embed import embed_minimal_html, embed_data
slider = IntSlider(value=40)
embed_minimal_html('new.html', views=[slider], title='Widgets export')


from ipywidgets import IntSlider
from ipywidgets.embed import embed_minimal_html, embed_data
from IPython.display import HTML
# container = HTML('<div id="my-widget-container"></div>')
# data = embed_data(report)
# container.value = f'<script type="application/vnd.jupyter.widget-view+json">{data}</script>'


data["manager_state"]

{'version_major': 2,
 'version_minor': 0,
 'state': {'1dc6cc33d528457e85a6c305ff4a43c0': {'model_name': 'FigureModel',
   'model_module': 'jupyterlab-plotly',
   'model_module_version': '^5.13.1',
   'state': {'_config': {'plotlyServerURL': 'https://plot.ly'},
    '_data': [{'alignmentgroup': 'True',
      'hoverinfo': 'x',
      'legendgroup': 'Entry level',
      'marker': {'color': '#FF7F0E'},
      'name': 'Entry level',
      'notched': False,
      'offsetgroup': 'Entry level',
      'orientation': 'h',
      'showlegend': True,
      'x': {'dtype': 'float64', 'shape': (976,)},
      'x0': ' ',
      'xaxis': 'x',
      'y0': ' ',
      'yaxis': 'y',
      'type': 'box',
      'uid': 'c76cfe63-000c-46db-8ef5-8d65805a782a'},
     {'alignmentgroup': 'True',
      'hoverinfo': 'x',
      'legendgroup': 'Associate',
      'marker': {'color': '#2CA02C'},
      'name': 'Associate',
      'notched': False,
      'offsetgroup': 'Associate',
      'orientation': 'h',
      'showlegend': True,
      'x': {'dtype': 'float64', 'shape': (1181,)},
      'x0': ' ',
      'xaxis': 'x',
      'y0': ' ',
      'yaxis': 'y',
      'type': 'box',
      'uid': '0b024056-e262-41ca-acc0-73fccc60a03b'},
     {'alignmentgroup': 'True',
      'hoverinfo': 'x',
      'legendgroup': 'Mid-Senior level',
      'marker': {'color': '#D62728'},
      'name': 'Mid-Senior level',
      'notched': False,
      'offsetgroup': 'Mid-Senior level',
      'orientation': 'h',
      'showlegend': True,
      'x': {'dtype': 'float64', 'shape': (1682,)},
      'x0': ' ',
      'xaxis': 'x',
      'y0': ' ',
      'yaxis': 'y',
      'type': 'box',
      'uid': '24f47108-9ca3-43b6-a5b9-bc9766a04582'},
     {'alignmentgroup': 'True',
      'hoverinfo': 'x',
      'legendgroup': 'Executive',
      'marker': {'color': '#9467BD'},
      'name': 'Executive',
      'notched': False,
      'offsetgroup': 'Executive',
      'orientation': 'h',
      'showlegend': True,
      'x': {'dtype': 'float64', 'shape': (2,)},
      'x0': ' ',
      'xaxis': 'x',
      'y0': ' ',
      'yaxis': 'y',
      'type': 'box',
      'uid': '5ede6670-f04e-4f8b-80f9-cb5786c15f88'}],
    '_dom_classes': (),
    '_js2py_layoutDelta': None,
    '_js2py_pointsCallback': {},
    '_js2py_relayout': None,
    '_js2py_restyle': {},
    '_js2py_traceDeltas': None,
    '_js2py_update': {},
    '_last_layout_edit_id': 1,
    '_layout': {'annotations': [{'ax': 0,
       'ay': 0,
       'font': {'size': 16},
       'text': 'Sample Size: 965',
       'x': 1.1,
       'xanchor': 'center',
       'xref': 'paper',
       'y': -0.1,
       'yanchor': 'top',
       'yref': 'paper'}],
     'boxmode': 'group',
     'legend': {'title': {'text': 'Seniority level'}, 'tracegroupgap': 0},
     'margin': {'t': 60},
     'showlegend': True,
     'template': {'data': {'bar': [{'hovertemplate': '<b>%{x}</b><br><i>Count</i>: %{y}',
         'type': 'bar'}],
       'histogram': [{'hovertemplate': '<b>%{x}</b><br><i>Count</i>: %{y}',
         'type': 'histogram'}],
       'pie': [{'hovertemplate': '<b>%{label}</b><br><i>Count</i>: %{value}',
         'type': 'pie'}],
       'scatter': [{'hovertemplate': '<b>%{x}</b><br><i>Count</i>: %{y}',
         'type': 'scatter'}]},
      'layout': {'colorway': ['#1F77B4',
        '#FF7F0E',
        '#2CA02C',
        '#D62728',
        '#9467BD',
        '#8C564B',
        '#E377C2',
        '#7F7F7F',
        '#BCBD22',
        '#17BECF'],
       'dragmode': 'pan',
       'font': {'size': 16},
       'legend': {'font': {'size': 17}},
       'paper_bgcolor': 'rgb(171,200,227)',
       'plot_bgcolor': '#BAE0F3',
       'showlegend': False,
       'title': {'font': {'size': 19}},
       'uniformtext': {'minsize': 12, 'mode': 'hide'},
       'xaxis': {'showgrid': False, 'zeroline': False},
       'yaxis': {'zeroline': False}}},
     'title': {'text': 'Salary (Lower Bound) Based on Seniority Level'},
     'xaxis': {'anchor': 'y',
      'domain': [0.0, 1.0],
      'showgrid': True,
      'title': {'text': 'salary_lb'}},
     'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0]},
     'autosize': True},
    '_py2js_addTraces': {},
    '_py2js_animate': {},
    '_py2js_deleteTraces': {},
    '_py2js_moveTraces': {},
    '_py2js_relayout': None,
    '_py2js_removeLayoutProps': {},
    '_py2js_removeTraceProps': {},
    '_py2js_restyle': {},
    '_py2js_update': {},
    '_view_count': 1},
   'buffers': [{'encoding': 'base64',
     'path': ['_data', 0, 'x', 'buffer'],
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     'path': ['_data', 3, 'x', 'buffer'],
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    'layout': 'IPY_MODEL_d25623c37b114758a9b49ddd25ef6ab4'}},
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    'padding': '0px 0px 0px 0px'}},
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   'model_module': '@jupyter-widgets/controls',
   'model_module_version': '2.0.0',
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    'children': ['IPY_MODEL_7727ef5a475040dab81f53ba1a468fba',
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embed_minimal_html('export.html', views=[time_chart], title='Widgets export')


# a = np.array([0,1,2])
# b = np.array([0,0,0])
# a = a[:, np.newaxis]
# b = b[:, np.newaxis]


# np.concatenate((b,a), axis=1)


# stemmer = SnowballStemmer('english')
# lemmatizer = WordNetLemmatizer()


# lemmatizer.lemmatize("hellothere")


import nltk
CHARS_TO_KEEP = "\w$%#" # Characters that should be kept because they bring semantic value
english_vocab = set(w.lower() for w in nltk.corpus.words.words())

def is_between(a : int,  value : int, b : int):
    '''
        a: lower bound
        b: upper bound
        value: value to test bound
    '''
    return a <= value <= b

assert is_between(1, 1.5, 2) == True
assert is_between(1, 3, 2) == False

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
c:\Users\harri\Documents\Programming\linkedin_data_analysis\PLotLee_Mini\linkedin-job-data-analysis_subplots_test.ipynb Cell 81 line 1
----> <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y142sZmlsZQ%3D%3D?line=0'>1</a> import nltk
      <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y142sZmlsZQ%3D%3D?line=1'>2</a> CHARS_TO_KEEP = "\w$%#" # Characters that should be kept because they bring semantic value
      <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y142sZmlsZQ%3D%3D?line=2'>3</a> english_vocab = set(w.lower() for w in nltk.corpus.words.words())

ModuleNotFoundError: No module named 'nltk'


def deal_with_word_match_overlap(lc_word_match : re.Match, uc_word_match: re.Match,
                                index_num : int, should_segment=True) -> int:
    '''
       lc_word_match : first_match
       uc_word_match : second_match
       index_num : row index number of our df DataFrame
    
       returns: 
           overlap State (tells us which iterator to advance):
               state of 1 means second_match precedes first match
               state of 0 means second_match overlaps first match
              state of -1 means second_match succeeds first match 
           valid string: word that should be added to our final array
    '''
    overlap_state = -99
    valid_string = ""
    
    ## Overlap found. That means only one string is valid 
    if is_between(lc_word_match.start(), uc_word_match.start(), lc_word_match.end()) or \
    is_between(lc_word_match.start(), uc_word_match.end(), lc_word_match.end()):    
        if lc_word_match.string.lower() in english_vocab:
            valid_string = lc_word_match.group()
        elif uc_word_match.string.lower() in english_vocab:
            valid_string = uc_word_match.group()
        else:
            if should_segment is True:
                if len(lc_word_match.group()) < len(uc_word_match.group()):
                    valid_string = segment(uc_word_match.group())
                else:
                    valid_string = segment(lc_word_match.group())
#             print("----------------------------- WARNING -----------------------------")
#             print(f"Row {index_num} with LC_match {lc_word_match}")
#             print(f"and UC_match {uc_word_match} did not produce a valid string")
#             print(f"Proceeding to assign valid_string to lc_word_match string")
#             print("-------------------------------------------------------------------", end="\n\n")
            else:
                if len(lc_word_match.group()) < len(uc_word_match.group()):
                    valid_string = uc_word_match.group()
                else:
                    valid_string = lc_word_match.group()
        overlap_state = 0
    elif uc_word_match.start() >= lc_word_match.start():
        valid_string = lc_word_match.group()
        overlap_state = -1
    elif uc_word_match.start() <= lc_word_match.start():
        valid_string = uc_word_match.group()
        overlap_state = 1
        
    return valid_string, overlap_state


## This should also find all lowercase words
def find_all_words_based_on_delim_and_capital_letters(row, test=False):
    '''
        Assumption: Only segment words which include capital letters
    '''
    job_description = row['description'] if test is False else row
    lc_word_generators = itertools.tee(re.finditer(f"[{CHARS_TO_KEEP}]+", job_description))
    uc_word_generators = itertools.tee(re.finditer("[A-Z]{1}\w+", job_description))
    lc_word_generator_length = sum(1 for elem in lc_word_generators[0])
    uc_word_generator_length = sum(1 for elem in uc_word_generators[0])
    final_words_array = []
    
    lc_match = next(lc_word_generators[1])
    uc_match = next(uc_word_generators[1])
    init_lc_index = 0
    init_uc_index = 0
    overlap_state = -99

    while init_lc_index < lc_word_generator_length and \
          init_uc_index < uc_word_generator_length:
            # Finally, increment iterator if the above condition fits
            if overlap_state == 1:
                uc_match = next(uc_word_generators[1])
            elif overlap_state == 0:
                lc_match = next(lc_word_generators[1])
                uc_match = next(uc_word_generators[1])                
            elif overlap_state == -1:
                lc_match = next(lc_word_generators[1])
            
            # First, check overlap status
            valid_string, overlap_state = deal_with_word_match_overlap(
                lc_match,
                uc_match, 
                0) # Use 0 instead of row.name for now
        
            if type(valid_string) == list:
                final_words_array.extend(valid_string)
            else:
                final_words_array.append(valid_string)
            
            # Based on overlap status, plan iterator increment
            if overlap_state == 1:
                init_uc_index += 1

            elif overlap_state == 0:
                ## Only keep one of the generator words if they overlap each other
                init_lc_index += 1
                init_uc_index += 1

            elif overlap_state == -1:
                init_lc_index += 1
    
    try:
        if init_lc_index < lc_word_generator_length:
            while True:
                final_words_array.append(next(lc_word_generators[1]).group())

        elif init_uc_index < uc_word_generator_length:
            while True:
                final_words_array.append(next(uc_word_generators[1]).group())
    except StopIteration:
        pass

    np_final_word_array = np.expand_dims(np.array(final_words_array), axis=1)
    np_job_description_index = np.expand_dims([0] * len(final_words_array), axis=1)
    return np.concatenate((np_job_description_index, np_final_word_array), axis=1).tolist()



# text = "On average, I would hire a lot of coolcatseatingspaghetti for this"
# print(find_all_words_based_on_delim_and_capital_letters(text, test=True))

# text = "The candidate is expected to show up to all meetingsKey responsibilities"
# print(find_all_words_based_on_delim_and_capital_letters(text, test=True))

# words_based_on_delim = df.head(5).apply(find_all_words_based_on_delim_and_capital_letters, axis=1).explode()


def find_all_words_based_on_normal_delim(row, test=False):
    '''
        Assumption: Segment every word
    '''
    job_description = row["description"] if test is False else row
    # Segment every word. Can be inefficient! How do we avoid this
    all_words = []
    for word in re.findall(f"[{CHARS_TO_KEEP}]+", job_description):
        try:
            words = segment(word) ## ValueError can occur due to unavailable segmentation
            all_words.extend(words)
        except ValueError:
            all_words.append(word)
    final_words_array = [stemmer.stem(word) for word in all_words if word.lower() not in STOPWORDS]
    np_final_word_array = np.expand_dims(np.array(final_words_array), axis=1)
    np_job_description_index = np.expand_dims([row.name if test is False else 0] 
                                              * len(final_words_array), axis=1)
    return np.concatenate((np_job_description_index, np_final_word_array), axis=1).tolist()


all_words = df.swifter.apply(find_all_words_based_on_normal_delim, axis=1).explode()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
c:\Users\harri\Documents\Programming\linkedin_data_analysis\PLotLee_Mini\linkedin-job-data-analysis_subplots_test.ipynb Cell 85 line 2
     <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=15'>16</a>     np_job_description_index = np.expand_dims([row.name if test is False else 0] 
     <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=16'>17</a>                                               * len(final_words_array), axis=1)
     <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=17'>18</a>     return np.concatenate((np_job_description_index, np_final_word_array), axis=1).tolist()
---> <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=20'>21</a> all_words = df.swifter.apply(find_all_words_based_on_normal_delim, axis=1).explode()

File c:\Users\harri\miniconda3\envs\py310_notebook_env\lib\site-packages\swifter\swifter.py:419, in DataFrameAccessor.apply(self, func, axis, raw, result_type, args, **kwds)
    417 try:  # try to vectorize
    418     with suppress_stdout_stderr_logging():
--> 419         tmp_df = func(sample, *args, **kwds)
    420         sample_df = sample.apply(func, axis=axis, raw=raw, result_type=result_type, args=args, **kwds)
    421         self._validate_apply(
    422             np.array_equal(sample_df, tmp_df) & (hasattr(tmp_df, "shape")) & (sample_df.shape == tmp_df.shape),
    423             error_message=("Vectorized function sample does not match pandas apply sample."),
    424         )

c:\Users\harri\Documents\Programming\linkedin_data_analysis\PLotLee_Mini\linkedin-job-data-analysis_subplots_test.ipynb Cell 85 line 8
      <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=5'>6</a> # Segment every word. Can be inefficient! How do we avoid this
      <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=6'>7</a> all_words = []
----> <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=7'>8</a> for word in re.findall(f"[{CHARS_TO_KEEP}]+", job_description):
      <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=8'>9</a>     try:
     <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y146sZmlsZQ%3D%3D?line=9'>10</a>         words = segment(word) ## ValueError can occur due to unavailable segmentation

NameError: name 'CHARS_TO_KEEP' is not defined


# !pip freeze | grep spacy


# !python -m spacy download en_core_web_sm


import spacy
nlp = spacy.load("en_core_web_sm")


# [token.lemma_ for token in doc]


def find_all_words_based_on_spacy_delim(row, test=False):
    '''
        Assumption: Segment every word
    '''
    job_description = row["description"] if test is False else row
    nlp_tokens = nlp(job_description)
    all_words = [token.lemma_ for token in nlp_tokens if token.lemma_.lower() not in STOPWORDS] 
#     final_words_array = []
# ##    Ignore segmentation for now because we may not need it
#     for word in all_words:
#         if word not in nlp_tokens.ents:
#             try:
#                 words = segment(word) ## ValueError can occur due to unavailable segmentation
#                 final_words_array.extend(words)
#             except ValueError:
#                 final_words_array.append(word)
#         else:
#             final_words_array.append(word)
                
    np_final_word_array = np.expand_dims(np.array(all_words), axis=1)
    np_job_description_index = np.expand_dims([row.name if test is False else 0] 
                                              * len(all_words), axis=1)
    return np.concatenate((np_job_description_index, np_final_word_array), axis=1).tolist()


all_words = df.swifter.apply(find_all_words_based_on_spacy_delim, axis=1).explode()

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
c:\Users\harri\Documents\Programming\linkedin_data_analysis\PLotLee_Mini\linkedin-job-data-analysis_subplots_test.ipynb Cell 91 line 2
     <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=20'>21</a>     np_job_description_index = np.expand_dims([row.name if test is False else 0] 
     <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=21'>22</a>                                               * len(all_words), axis=1)
     <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=22'>23</a>     return np.concatenate((np_job_description_index, np_final_word_array), axis=1).tolist()
---> <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=25'>26</a> all_words = df.swifter.apply(find_all_words_based_on_spacy_delim, axis=1).explode()

File c:\Users\harri\miniconda3\envs\py310_notebook_env\lib\site-packages\swifter\swifter.py:419, in DataFrameAccessor.apply(self, func, axis, raw, result_type, args, **kwds)
    417 try:  # try to vectorize
    418     with suppress_stdout_stderr_logging():
--> 419         tmp_df = func(sample, *args, **kwds)
    420         sample_df = sample.apply(func, axis=axis, raw=raw, result_type=result_type, args=args, **kwds)
    421         self._validate_apply(
    422             np.array_equal(sample_df, tmp_df) & (hasattr(tmp_df, "shape")) & (sample_df.shape == tmp_df.shape),
    423             error_message=("Vectorized function sample does not match pandas apply sample."),
    424         )

c:\Users\harri\Documents\Programming\linkedin_data_analysis\PLotLee_Mini\linkedin-job-data-analysis_subplots_test.ipynb Cell 91 line 6
      <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=1'>2</a>     '''
      <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=2'>3</a>         Assumption: Segment every word
      <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=3'>4</a>     '''
      <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=4'>5</a>     job_description = row["description"] if test is False else row
----> <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=5'>6</a>     nlp_tokens = nlp(job_description)
      <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=6'>7</a>     all_words = [token.lemma_ for token in nlp_tokens if token.lemma_.lower() not in STOPWORDS] 
      <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=7'>8</a> #     final_words_array = []
      <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=8'>9</a> # ##    Ignore segmentation for now because we may not need it
     <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=9'>10</a> #     for word in all_words:
   (...)
     <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=16'>17</a> #         else:
     <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y155sZmlsZQ%3D%3D?line=17'>18</a> #             final_words_array.append(word)

NameError: name 'nlp' is not defined


all_words = all_words.str[1]


words_based_on_delim = pd.DataFrame({"Label_ID": all_words.str[0], 
                                    "Word" : all_words.str[1]})

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
c:\Users\harri\Documents\Programming\linkedin_data_analysis\PLotLee_Mini\linkedin-job-data-analysis_subplots_test.ipynb Cell 94 line 1
----> <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y161sZmlsZQ%3D%3D?line=0'>1</a> words_based_on_delim = pd.DataFrame({"Label_ID": all_words.str[0], 
      <a href='vscode-notebook-cell:/c%3A/Users/harri/Documents/Programming/linkedin_data_analysis/PLotLee_Mini/linkedin-job-data-analysis_subplots_test.ipynb#Y161sZmlsZQ%3D%3D?line=1'>2</a>                                     "Word" : all_words.str[1]})

NameError: name 'all_words' is not defined


words_based_on_delim.head(10)


%%timeit
def get_keywords(row):
    job_desc = row["description"].lower()
    has_qualification = job_desc.find("qualification")
    has_requirement = job_desc.find("requirement")
    has_basic_requirement = job_desc.find("basic requirement")
    has_responsibility = job_desc.find("responsibility")
    return [has_qualification, has_requirement, has_basic_requirement, has_responsibility]

new_data = df.apply(get_keywords, result_type="expand", axis=1)


new_data = df.apply(get_keywords, result_type="expand", axis=1)


new_data.head(5)


def get_keywords_series(job_desc):
    job_desc = job_desc.lower()
    has_qualification = job_desc.find("qualification")
    has_requirement = job_desc.find("requirement")
    has_basic_requirement = job_desc.find("basic requirement")
    has_responsibility = job_desc.find("responsibilit")
    return [has_qualification, has_requirement, has_basic_requirement, has_responsibility]


%%timeit
series = df["description"].apply(get_keywords_series)
new_data_series = pd.DataFrame(series.tolist())


series = df["description"].apply(get_keywords_series)
new_data_series = pd.DataFrame(series.tolist(), columns=["has_qualification", "has_requirement",
                                                        "has_basic_requirement", "has_responsibility"])


new_data_series.head(5)


from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
 
word_tokens = word_tokenize(example_sent)
# converts the words in word_tokens to lower case and then checks whether
# they are present in stop_words or not
filtered_sentence = [w for w in word_tokens if not w.lower() in stop_words]
#with no lower case conversion
filtered_sentence = []
 
for w in word_tokens:
    if w not in stop_words:
        filtered_sentence.append(w)
 
print(word_tokens)
print(filtered_sentence)

	salary_lb	salary_ub	salary_text
0	NaN	NaN	NaN
1	NaN	NaN	NaN
2	NaN	NaN	NaN
3	NaN	NaN	NaN
4	NaN	NaN	NaN

	salary_lb	salary_ub
count	965.000000	965.000000
mean	50985.354238	61313.719047
std	49165.472000	58034.939059
min	22.000000	24.000000
25%	40.000000	60.000000
50%	60000.000000	80000.000000
75%	90000.000000	115000.000000
max	135000.000000	155000.000000

	salary_lb	salary_ub
count	411.000000	411.000000
mean	42.109100	52.807494
std	12.739683	19.832016
min	22.000000	24.000000
25%	30.000000	33.000000
50%	40.000000	60.000000
75%	50.000000	60.000000
max	135.000000	145.000000

	salary_lb	salary_ub	salary_text
198	5800.0	6000.0	$5800.00-$6000.00
284	5800.0	6000.0	$5800.00-$6000.00
346	5800.0	6000.0	$5800.00-$6000.00
438	5800.0	6000.0	$5800.00-$6000.00
486	5800.0	6000.0	$5800.00-$6000.00
586	5800.0	6000.0	$5800.00-$6000.00
686	5800.0	6000.0	$5800.00-$6000.00
738	5800.0	6000.0	$5800.00-$6000.00
798	5800.0	6000.0	$5800.00-$6000.00
886	5800.0	6000.0	$5800.00-$6000.00
936	5800.0	6000.0	$5800.00-$6000.00
1084	5800.0	6000.0	$5800.00-$6000.00
1134	5800.0	6000.0	$5800.00-$6000.00
1332	5800.0	6000.0	$5800.00-$6000.00
1382	5800.0	6000.0	$5800.00-$6000.00
1532	5800.0	6000.0	$5800.00-$6000.00
1582	5800.0	6000.0	$5800.00-$6000.00
1636	5800.0	6000.0	$5800.00-$6000.00
1748	5800.0	6000.0	$5800.00-$6000.00
1782	5800.0	6000.0	$5800.00-$6000.00
1894	5800.0	6000.0	$5800.00-$6000.00

0. Setup¶

1. Data Exploration & Cleaning¶

1.1 JSON Normalization¶

Some Seniority Levels are "Not Applicable"¶

1.3 Wandb Exploration¶

1.4 Exploring Columns¶

1.5 Make Our Linkedin Job Analysis Dashboard¶

3. Extract Words From Job Description¶

Overlap Delim (Bad Idea by Me)¶

Normal Delim¶

Spacy Delim¶

Archive¶

Performance Testing between pd.Series.apply and pd.DataFrame.apply¶

Performance Test End¶

	title	company	description	onsite_remote	salary	location	criteria	posted_date	link	country
0	Data Analyst - Recent Graduate	PayPal	At PayPal (NASDAQ: PYPL), we believe that ever...	onsite	NaN	Buffalo-Niagara Falls Area	[{'Seniority level': 'Not Applicable'}, {'Empl...	2022-11-22	https://www.linkedin.com/jobs/view/data-analys...	USA
1	Data Analyst - Recent Graduate	PayPal	At PayPal (NASDAQ: PYPL), we believe that ever...	onsite	NaN	San Jose, CA	[{'Seniority level': 'Not Applicable'}, {'Empl...	2022-11-22	https://www.linkedin.com/jobs/view/data-analys...	USA
2	Data Analyst	PayPal	At PayPal (NASDAQ: PYPL), we believe that ever...	onsite	NaN	Texas, United States	[{'Seniority level': 'Not Applicable'}, {'Empl...	2022-11-17	https://www.linkedin.com/jobs/view/data-analys...	USA
3	Data Analyst	PayPal	At PayPal (NASDAQ: PYPL), we believe that ever...	onsite	NaN	Illinois, United States	[{'Seniority level': 'Not Applicable'}, {'Empl...	2022-11-17	https://www.linkedin.com/jobs/view/data-analys...	USA
4	Entry-Level Data Analyst	The Federal Savings Bank	The Federal Savings Bank, a national bank and ...	onsite	NaN	Chicago, IL	[{'Seniority level': 'Entry level'}, {'Employm...	2022-11-17	https://www.linkedin.com/jobs/view/entry-level...	USA

	salary_ub
Seniority level
Associate	102122.623907
Entry level	61621.880342
Executive	NaN
Mid-Senior level	122456.738164